Case Studies in Bayesian Statistics

Volume III

Häftad, Engelska, 1997

Av Constantine Gatsonis, James S. Hodges, Robert E. Kass, Robert E. McCulloch, Peter Rossi, Nozer D. Singpurwalla

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This third volume of case studies presents detailed applications of Bayesian statistical analysis, emphasizing the scientific context. The papers were presented and discussed at a workshop at Carnegie-Mellon University in October, 1995. In this volume, which is dedicated to the memory of Morrie Groot, econometric applications are highlighted. There are six invited papers, each with accompanying invited discussion, and nine contributed papers.

Produktinformation

Utgivningsdatum1997-06-05
Mått155 x 235 x 27 mm
Vikt750 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Statistics
Antal sidor471
FörlagSpringer-Verlag New York Inc.
ISBN9780387949901

Tillhör följande kategorier

Invited Papers (with discussion).- 1. Probing Public Opinion: the State of Valencia Experience.- 2. Pressure Matching for Hydrocarbon Reservoirs: A Case Study in the Use of Bayes Linear Strategies for Large Computer Experiments.- 3. Hierarchical Bayes Models for Micro-Marketing Strategies.- 4. Modeling Mortality Rates for Elderly Heart Attack Patients: Profiling Hospitals in the Cooperative Cardiovascular Project.- 5. A Bayesian Approach to the Modeling of Spatial-Temporal Precipitation Data.- 6. Variable Selection Tests of Asset Pricing Models.- Contributed Papers.- 7. Modeling the History of Diabetic Retinopathy.- 8.Hierarchical Bayesian Analysis for Prevalence Estimation.- 9. Estimating the Cells of a Contingency Table with Limited Information, for Use in Geodemographic Marketing.- 10. Multiresolution Assessment of Forest Inhomogeneity.- 11. Assessment of Deleterious Gene Models Using Predictive p-values.- 12. Bayesian Inference for the Best Ordinal Multinomial Population in a Taste Test.- 13. A Random Effects Multinomial Probit Model of Car Ownership Choice.- 14. Changepoint Modeling of Longitudinal PSA as a Biomarker for Prostate Cancer.- 15. A Subjective Bayesian Approach to Environmental Sampling.